Auditable privacy protection deep learning platform construction method based on block chain incentive mechanism

ABSTRACT

Disclosed is a method for constructing an auditable and privacy-preserving collaborative deep learning platform based on a blockchain-empowered incentive mechanism, which allows trainers of multiple similar models to cooperate for training deep learning models while protecting confidentiality and auditing correctness of shared parameters. The invention has the following technical effects. Firstly, the encryption method used by model trainers protects the confidentiality of sharing parameters; furthermore, the updated parameters are decrypted through the cooperation of all participants, which reduces the possible disclosure of parameters. Secondly, the encrypted parameters are stored in the blockchain, and are only available to participants and authorized miners who are responsible to update parameters. Thirdly, the blockchain-based incentive mechanism guarantees the validity of the parameters, where collaborative trainers need to pay deposit when uploading parameters at the beginning and then the shared parameters can be validated. Concretely, if the parameters are invalid, the deposit would be forfeited.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of priority from Chinese PatentApplication No. 201811471842.X, filed on Dec. 4, 2018. The content ofthe aforementioned applications, including any intervening amendmentsthereto, are incorporated herein by reference.

TECHNICAL FIELD

The invention relates to cyberspace security, more particularly to amethod for constructing an auditable and privacy-preservingcollaborative deep learning platform based on a blockchain-empoweredincentive mechanism.

BACKGROUND OF THE INVENTION

1. Deep Learning

Deep learning is a subfield of machine learning which enables practicalapplications of machine learning. Machine learning basically usesalgorithms to perform data analysis and then makes decisions andpredictions in real applications. As to learning, training is realizedby using a large amount of data, from which how to complete a task viavarious algorithms is learned.

Training in the deep learning is actually directed to a neural networkwhich consists of millions of neurons that are connected according to acertain rule and laid out in layers. For example, the leftmost layer isreferred to input layer for receiving input data, and the rightmostlayer is referred to output layer from which output data of the neuralnetwork is obtained. Further, there are hidden layers that are invisibleto the outside between the input layer and the output layer. Theconnection weight between two neurons is a parameter of the trainingmodel what the model needs to learn, and backpropagation algorithm isone of training algorithms for the neural network.

The backpropagation algorithm mainly consists of incentive propagationand weight update phases. In the incentive propagation, incentiveresponse is obtained upon training inputs into the network. There is adeviation to be calculated between the incentive response and output ofinterest corresponding to the training inputs, and the deviation iscalled a response error between the hidden layer and the output layer.Next, the weight is updated using the calculated response error.Particularly, the input incentive and the response error are multipliedto obtain a weighted gradient, which is then multiplied by a ratio andinverted to add to the weight. The two phases are iteratively repeateduntil the network response to inputs is within a satisfactory andpredetermined range to complete the training.

2. Blockchain

Blockchain as a core technology of bitcoin is a decentralized ledger.That is, a central institution will not be involved in the bookkeeping.Traditionally, the bookkeeping is done by the bank so as to ensure thesafety and effectiveness of the ledger. However, obviously, thecentralized ledger has some defects. For example, crisis or evencollapse of the entire system will occur upon the problems in thecentral institution such as being tampered or damaged; however, theblockchain can overcome those defects of the centralized ledger. But atthe same time, how to reach the consistency of accounts or an agreementof individual nodes (i.e., computers) in the blockchain is a key issueto be solved.

Inconsistency

The bitcoin has solved the inconsistency of the decentralized accountingsystem by means of competitive bookkeeping. The competitive bookkeepingis a mechanism in which each node competes for bookkeeping rightsaccording to their hashrate. In the bitcoin system, a round ofcompetition is held over the hashrate about every ten minutes, and thewinner of the competition gets the right to perform bookkeeping togenerate a new block in the blockchain. In other words, only the winnerof the competition is allowed to synchronize new account information ina certain period of time to other nodes while performing bookkeeping. Inthe bitcoin system, all the nodes reach the consensus through thecompetition of hashrate which essentially is the Proof of Workmechanism. In other blockchain systems, however, there are many otheralgorithms such as Proof of Stake, Delegated Proof of Stake, andByzantine fault tolerance to reach consensus among the nodes.

Incentive

There are costs of hashrate competition, obviously. Nodes needincentives to perform competitive motivation. In the design of SatoshiNakamoto protocol, the system will give a certain amount of bitcoin tothe winning node of each round as a reward when it completes thebookkeeping. Such computing process is referred to as mining, and thecomputers that perform the operation are referred to as miners.

Immutability of Transactions

The ledgers in the bitcoin system record transactions. However, unlikethe traditional models, delete operation is not available in theblockchain. A backup of corresponding block is created at all nodes whena transaction is uploaded to the blockchain. The data cannot be temperedunless all nodes are tampered at the same time, and thus tempering by nomeans succeeds. As a consequence, transactions in the blockchain cannotbe tampered.

3. Homomorphic Encryption

Ordinary algorithms encrypt data into ciphertext to protect dataconfidentiality. Unfortunately, nonsensical code will be createdgenerally when the operation result of two encrypted data is decrypted.

Homomorphic encryption is a form of encryption function that allowscomputation on ciphertexts, generating an encrypted result which, whendecrypted, matches the result of the operations as if they had beenperformed on the plaintext. In this way, the homomorphic encryption canachieve both the protection for data confidentiality and the computationof the encrypted data on the plain text.

The Paillier encryption algorithm is an algorithm for implementing thehomomorphic encryption, which satisfies additive homomorphism, i.e.,Encrypt (A)*Encrypt (B)=Encrypt (A+B).

4. Comprehensive Application

The output parameters need to be shared at each step of the training soas to achieve collaborative deep learning. The Paillier algorithm can beused to encrypt the parameters to ensure the confidentiality andupdateability of the parameters. In order to ensure the auditability ofparameters and facilitate the updating of parameters, the data isuploaded to the blockchain for processing based on the immutability andthe incentive method of the blockchain. As a consequence, according tothe above three methods, the collaborative deep learning is performedwhile the parameters are auditable and the confidentiality forparameters are guaranteed.

SUMMARY OF THE INVENTION

An object of the present invention is to provide a method forconstructing an auditable and privacy-preserving collaborative deeplearning platform based on a blockchain-empowered incentive mechanism.This invention is to encourage collaborators to share their parametersin the collaborative deep learning with blockchain-empowered incentives.Also, this invention is to overcome security problems and thereliability of parameters in the parameter sharing process of deeplearning. Concretely, security problems refer to the confidentiality ofsharing parameters and fairness of collaboration. The reliability ofparameters requires that sharing parameters are correctly encryptedfollowing the Paillier encryption algorithm. The security problems andthe reliability are realized among collaborators without a trustedthird-party platform.

According to the present invention, a method for constructing anauditable and privacy-preserving collaborative deep learning platformbased on a blockchain-empowered incentive mechanism, comprising:

-   -   encapsulating blockchain task interfaces for parameter sharing        of a collaborative deep learning scenario based on an        open-source blockchain platform Corda, wherein an intelligent        state content is agreed by multiple nodes throughout a network;    -   constructing a task execution flow model based on a blockchain        technology; uploading sharing parameters by participants        according to the task execution flow model; encapsulating the        uploaded sharing parameters into blocks, and linking the blocks        to form a blockchain, or obtaining updated sharing parameters        from the blockchain; processing the uploaded sharing parameters        based on the task execution flow model; encapsulating the        updated sharing parameters into blocks, and linking the blocks        to form the blockchain to gain a reward; acquiring the sharing        parameters that are updated from the blockchain;    -   building a parameter sharing platform for the collaborative deep        learning scenario; wherein the parameter sharing platform is        categorized into three layers comprising an encryption layer, a        blockchain layer, and a training algorithm layer;    -   encrypting the sharing parameters at the encryption layer by a        model trainer, and then transmitting the sharing parameters to        the blockchain layer through a transmission interface; wherein        parameter sharing information of the model trainer is recorded        in the blockchain layer; after the sharing parameters are        uploaded, miners read the encrypted sharing parameters through        the transmission interface for calculating and updating the        sharing parameters; after the sharing parameters are updated,        the model trainer acquires the updated sharing parameters        through the transmission interface to train the model        repeatedly.

In some embodiments, the step of encapsulating the blockchain taskinterfaces for parameter sharing of the collaborative deep learningscenario based on the open-source blockchain platform Corda comprises:

-   -   1) evaluating existing blockchain platforms in terms of        throughput, storage capacity, the number of nodes, and whether        the existing blockchain platforms support a smart contract;    -   2) selecting a platform with preferred parameters as the        blockchain platform; and encapsulating general-purpose        interfaces to support uploading, updating and acquisition of        sharing parameters of deep learning parameter sharing.

In some embodiments, the task execution flow model comprises:

-   -   Param Upload Flow, comprising: user identity, administrator        identity, the encrypted sharing parameters and the amount of        deposit against malicious users; wherein an output of the Param        Upload Flow is a transaction that contains a parameter update        status, and is shared by an administrator and the participants,        and corresponding information is not available for other users;        the participants who behave dishonestly during the operation are        identified in the smart contract and corresponding amount of        deposit is forfeited;    -   Updated Param Flow, comprising: miner identity as an updater,        administrator identity, and updated encrypted sharing        parameters; wherein once results of the sharing parameters        updated by miners are verified, the updated sharing parameters        are continuously stored in the blockchain, and the miner gains        corresponding rewards;    -   Decrypt Share Flow, comprising: user identity, administrator        identity, shared keys that are agreed in advance to assist the        decryption, the amount of the deposit for guaranteeing credit,        and an identifier of the administrator; wherein the participants        each own a part of the shared keys; when all the shared keys        from the participants are collected, the participants use the        shared keys to call a decryption method in the encryption layer        to decrypt the updated sharing parameters, so that sharing        parameters required for the next training is obtained;    -   Updated Param Return Flow for sending parameter update        notifications to participants with permission after miner nodes        update the sharing parameters; and    -   Download Param Flow, by which the participants with permission        obtain updated sharing parameters.

In some embodiments, the task execution flow model comprises five flowtypes of external encapsulation interfaces through which the sharingparameters are uploaded or downloaded by the users and are updated bythe miners; the transaction generated after the flows run form a blockwhich is encapsulated and linked into the blockchain; wherein thesharing parameters from the interfaces are trained immediately, and theinterfaces are integrated by the users to automate the entire processwithout manual confirmation.

In some embodiments, in the encryption layer, participants encrypt thetraining parameters with the Paillier encryption algorithm, and decryptthe updated encrypted sharing parameters into original parameter forms;meanwhile, the encryption layer further provides static interfaces forminers to update the sharing parameters, and employers are allowed tocall the interfaces to update sharing parameters and gain rewards in theblockchain.

In some embodiments, in blockchain layer, Corda is selected as theblockchain platform and used to build and release a new generation ofdistributed applications CorDapps, and the processing flows are allowedto be written programmatically; users send requests to call each flow inthe blockchain layer through an RPC interface, and a transaction iscreated during the execution of the flow, and an old state is consumedand a new state is constructed; after filling of the information, theidentities of the participants are confirmed by signatures of theparticipants, and then a validity of the transaction is verified throughthe contract; if confirmed, the information is written into theblockchain layer, and the blockchain ensures a consistency of theinformation stored by all nodes based on a consensus mechanism providedby the Corda.

In some embodiments, the training algorithm layer acquires the updatedencrypted sharing parameters of the blockchain layer, and then decryptsthe updated encrypted sharing parameters through the encryption layer,and the new parameters obtained directly participate in the training ofthe training algorithm layer.

In some embodiments, the step of encrypting the sharing parameterscomprises: encrypting partial data of the training parameters using thePaillier encryption algorithm; wherein participants are allowed toencrypt privacy parameters when using their own keys, and the updatedsharing parameters are decrypted when shared keys of all theparticipants are combined.

Compared to the prior art, the invention has the following advantagesand effects:

-   -   (1) the encryption method used by the model trainer ensures the        confidentiality of the parameters, and the process of decrypting        the updated sharing parameters requires the cooperation of all        participants, thereby further reducing possible disclosure of        parameters;    -   (2) the encrypted sharing parameters are stored in the        blockchain in the form of state, and only participants and        authorized miners are allowed to access the encrypted sharing        parameters; and    -   (3) the existence of a blockchain-based incentive mechanism        ensures the validity of the sharing parameters; participants        need to pay deposit when uploading the sharing parameters; if        the sharing parameters are invalid, the deposit will be        forfeited, thereby ensuring the auditability of the sharing        parameters.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an interaction diagram of entities in an auditable andprivacy-preserving collaborative deep learning platform based on ablockchain-empowered incentive mechanism according to the presentinvention;

FIG. 2 is a schematic diagram of Param Upload Flow according to anembodiment of the present invention; and

FIG. 3 is a schematic diagram of Updated Param Flow according to anembodiment of the present invention.

DETAILED DESCRIPTION OF EMBODIMENTS

The technical solutions of the invention will be described in detailbelow with reference to the accompanying drawings and specificembodiments, from which the present invention becomes clearer.Obviously, the embodiment described herein is a part of the embodimentsof the present invention. Based on the embodiment of the invention, allother embodiments obtained by those skilled in the art without creativeefforts shall fall within the scope of the present invention.

EXAMPLES

The embodiments of the present invention are to encourage collaboratorsto share their parameters in the collaborative deep learning withblockchain-empowered incentives. Also, this invention is to overcomesecurity problems and the reliability of parameters in the parametersharing process of deep learning. Concretely, security problems refer tothe confidentiality of sharing parameters and fairness of collaboration.The reliability of parameters requires that sharing parameters arecorrectly encrypted following the Paillier encryption algorithm. Thesecurity problems and the reliability are realized among collaboratorswithout a trusted third-party platform. FIG. 1 shows an interactiondiagram of entities in an auditable and privacy-preserving collaborativedeep learning platform based on a blockchain-empowered incentivemechanism.

A parameter sharing model through flows is established based on ablockchain platform. Such model eliminates centralized server and is notrestricted by the third-party platform. In addition, the blockchainplatform ensures the immutability and traceability of data. In thisembodiment, a general parameter sharing template is designed. Then,model trainers upload the sharing parameters and miners process thesharing parameters based on the template. Also, a task execution flowmodel is designed. In this model, the sharing parameters uploaded byparticipants follow this flow to complete the entire life cycle. Theoverall structure of the platform is categorized into three layerscomprising an encryption layer, a blockchain layer and a trainingalgorithm layer, as shown in FIG. 2 . The sharing parameters areencrypted, by model trainer, in the encryption layer, and then aretransmitted to the blockchain layer through Remote Procedure Call (RPC)interfaces, and next the parameter sharing information of model trainersare recorded in the blockchain layer. After the parameters are uploaded,miners read the encrypted data through the transmission interface forcalculation, and accordingly, the sharing parameters are updated. Then,trainers obtain the updated sharing parameters through the transmissioninterfaces. Further, the model is repeatedly trained by the updatedsharing parameters.

This embodiment provides a method for constructing an auditable andprivacy-preserving collaborative deep learning platform based on ablockchain-empowered incentive mechanism, including the following steps.

-   -   1. Blockchain task interfaces for deep learning parameter        sharing scenarios are encapsulated based on the open-source        blockchain platform Corda, where an intelligent state content is        agreed by multiple nodes throughout a network.

In a certain embodiment, step 1 includes the following steps.

-   -   1) Existing blockchain platforms are evaluated in terms of        throughput, storage capacity, the number of nodes, and whether        the existing blockchain platforms support a smart contract.    -   2) A platform with preferred parameters is selected as the        blockchain platform; and general-purpose interfaces are        encapsulated to support uploading, updating and acquisition of        sharing parameters of deep learning parameter sharing.    -   2. A task execution flow model is constructed based on a        blockchain technology; parameters are uploaded by participants        according to the task execution flow model; the uploaded sharing        parameters are encapsulated into blocks, and then the blocks are        linked to form a blockchain, or updated sharing parameters are        obtained from the blockchain; the uploaded sharing parameters        are processed based on the task execution flow model, and then        the updated sharing parameters are encapsulated into blocks, and        finally the blocks are linked to form the blockchain to gain a        reward; the sharing parameters that are updated are acquired        from the blockchain.

In a certain embodiment, the execution flow model in step 2 includes thefollowing flows.

-   -   (1) Param Upload Flow (PUF), including: user identity,        administrator identity, encrypted sharing parameters and the        amount of deposit against malicious users; where an output of        the Param Upload Flow is a transaction that contains a parameter        update state, and is shared by an administrator and the        participants, and other users cannot obtain corresponding        information; the participants, who behave dishonestly during the        operation, are identified in the smart contract and        corresponding amount of deposit is forfeited.    -   (2) Updated Param Flow (UPF), including: miner identity as an        updater, the identity of the administrator, and updated        encryption parameter values; where once results of sharing        parameters updated by miners are verified, the updated sharing        parameters are continuously stored in the blockchain, and the        miner gains corresponding rewards; the UPF is shown in FIG. 3 .    -   (3) Decrypt Share Flow (DSF), including: user identity,        administrator identity, shared keys that are agreed in advance        to assist the decryption, the amount of the deposit for        guaranteeing credit, and an identifier of the administrator;        where the participants each own a part of the shared keys.

When all the shared keys from the participants are collected, theparticipants use the shared keys to call a decryption method in theencryption layer to decrypt the updated sharing parameters, so thatsharing parameters required for the next training is obtained.

-   -   (4) Updated Param Return Flow (UPRF) for sending parameter        update notifications to participants with permission after miner        nodes update the sharing parameters; and    -   (5) Download Param Flow (DPF), by which participants with        permission obtain updated sharing parameters.

The Updated Param Return Flow is usually used to notify participantsafter the sharing parameters are successfully updated. Once participantsobtain a notification, it is used as a credential to call the DownloadParam Flow to acquire the updated sharing parameters. It should be notedthat the sharing parameters herein are also encrypted, and the DecryptShare Flow is called to obtain the key for decryption.

The present invention provides five flow types of external encapsulationinterfaces through which the sharing parameters are uploaded ordownloaded by the users and updated by the miners; the transactionsgenerated after the flows run form a block which is encapsulated andlinked into the blockchain; wherein the sharing parameters from theinterfaces are trained immediately, and the interfaces are integrated bythe users to automate the entire process without manual confirmation.

-   -   3. A parameter sharing platform for parameter sharing of the        collaborative deep learning scenario is built: where the        platform is catagorized into three layers comprising an        encryption layer, a blockchain layer, and a training algorithm        layer.

In the platform, the encryption layer and the blockchain layer areisolated from the training algorithm layer; the encrypted sharingparameters to be updated and the updated encrypted sharing parametersare placed on the blockchain layer; instead of uploading to theblockchain platform, the original data of the participants' parametersand other information are kept by the users; the users encrypt theparameters themselves through the encryption layer, and then theencrypted data is uploaded to the blockchain layer for storage throughan external interface. Therefore, this not only prevents the insecurityof saving plaintext data, but also reduces the amount of calculation forupdating blockchain and improves the efficiency of system operation.Moreover, the updated sharing parameters can be downloaded to train thetraining algorithm layer.

The encryption layer allows participants to encrypt the trainingparameters using the Paillier encryption algorithm, and decrypt theupdated encrypted sharing parameters to obtain the original parameterform. The static interface can be directly called for encryption anddecryption, so that participants can call the given parameters withoutknowing the internal implementation of the encryption system accordingto the method, so as to quickly perform encryption and decryptionoperations, and connect with model training in a seamless manner. At thesame time, the encryption layer also provides a static interface forminers to update the sharing parameters, so that employers can call theinterface to update the sharing parameters and earn rewards in theblockchain.

The blockchain layer: in blockchain layer, Corda is selected as theblockchain platform and used to build and release a new generation ofdistributed applications CorDapps. and the processing flows are allowedto be written programmatically; users send requests to call each flow inthe blockchain layer through an RPC interface, and a transaction iscreated during the execution of the flow, and an old state is consumedand a new state is constructed; after information is filled, theparticipant indentifications are confirmed by signatures of theparticipants, and then a validity of the transaction is verified throughthe contract; if confirmed, the information is written into theblockchain layer, and the blockchain ensures a consistency of theinformation stored by all nodes based on a consensus mechanism providedby the Corda platform.

The training algorithm layer: the training algorithm layer obtains theupdated encrypted sharing parameters in the blockchain layer, and thenthe updated encrypted sharing parameters are decrypted through theencryption layer, and the new parameters directly participate in thetraining of the training algorithm layer, thereby reducing humanprocessing for the data. Participants only need to call thecorresponding upload and update interfaces, so that the process isautomatically carried out.

-   -   4. Personal privacy information and data are encrypted using an        identity-based encryption algorithm, where the personal privacy        information and data are only visible to authorized persons.

In the specific embodiment, in step 4, partial data of the trainingparameters is encrypted using the Paillier encryption algorithm;participants are allowed to encrypt privacy parameters when using theirown keys, and the updated sharing parameters are decrypted when theshared keys of all the participants are combined.

The above embodiment is a preferred embodiment of the invention, whichis not intended to limit the scope of the present invention. Anychanges, modifications, substitutions, combinations, simplificationswithout departing from the spirit and principle of the invention shallfall within the scope of the invention.

What is claimed is:
 1. A method for constructing an auditable andprivacy-preserving collaborative deep learning platform based on ablockchain-empowered incentive mechanism, comprising: encapsulatingblockchain task interfaces for deep learning parameter sharing scenariosbased on an open-source blockchain platform Corda, wherein anintelligent state content is agreed by multiple nodes throughout anetwork; constructing a task execution flow model based on a blockchaintechnology; uploading sharing parameters by participants according tothe task execution flow model; encapsulating the uploaded sharingparameters into blocks, and linking the blocks to form a blockchain, orobtaining the updated sharing parameters from the blockchain; processingthe uploaded sharing parameters based on the task execution flow model;encapsulating the updated sharing parameters into blocks, and linkingthe blocks to form the blockchain to gain a reward; acquiring thesharing parameters that are updated from the blockchain; building aparameter sharing platform for parameter sharing of the collaborativedeep learning scenarios; wherein the parameter sharing platform iscategorized into three layers comprising an encryption layer, ablockchain layer, and a training algorithm layer; wherein uploadingsharing parameters by participants according to the task execution flowmodel comprises: encrypting training parameters for training a model, atthe encryption layer by a model trainer as one of the participants, andthen transmitting, by the model trainer, the encrypted trainingparameters as the sharing parameters to the blockchain layer through atransmission interface; wherein parameter sharing information of themodel trainer is recorded in the blockchain layer; after the sharingparameters are uploaded to the blockchain, miners read the encryptedsharing parameters through the transmission interface for calculatingand updating the sharing parameters in the blockchain; after the sharingparameters are updated, the model trainer obtains the updated sharingparameters from the blockchain through the transmission interface totrain the model repeatedly with the updated sharing parameters, whereinencrypting training parameters comprises: encrypting partial data of thetraining parameters using the Paillier encryption algorithm; whereinparticipants are allowed to encrypt private training parameters whenusing a common public key, and the updated sharing parameters aredecrypted when shared key pieces respective to the public key's privatekey are combined from all the participants, wherein when all the sharedkey pieces from the participants are collected, one of the participantsuse the shared key pieces to call a decryption method in the encryptionlayer to decrypt the updated sharing parameters, so that the sharingparameters required for the next training is obtained for the one of theparticipants.
 2. The method of claim 1, wherein the step ofencapsulating the blockchain task interfaces for the deep learningparameter sharing scenarios based on the open-source blockchain platformCorda comprises: i) evaluating existing blockchain platforms in terms ofthroughput, storage capacity, the number of nodes, and whether theexisting blockchain platforms support a smart contract; and ii)selecting a platform with preferred parameters as the blockchainplatform; and encapsulating general-purpose interfaces to supportuploading, updating and acquisition of the sharing parameters of deeplearning parameter sharing.
 3. The method of claim 1, wherein the taskexecution flow model comprises: Param Upload Flow, comprising: useridentity, administrator identity, encrypted parameter values and theamount of deposit against malicious users; wherein an output of theParam Upload Flow is a transaction that contains a parameter updatestate, and is shared by an administrator and the participants, andcorresponding information is not available for other users; theparticipants who behave dishonestly during the operation are identifiedin the smart contract and corresponding amount of deposit is forfeited;Updated Param Flow, comprising: miner identity as an updater,administrator identity, and updated encrypted sharing parameters;wherein once results of the sharing parameters updated by miners areverified, the updated sharing parameters are continuously stored in theblockchain, and the miner gains corresponding rewards; Decrypt ShareFlow, comprising: user identity, administrator identity, shared keypieces that are agreed in advance to assist the decryption, the amountof the deposit for guaranteeing credit, and an identifier of theadministrator; wherein the participants each own a part of the sharedkey pieces; Updated Param Return Flow for sending parameter updatenotifications to participants with permission after miner nodes updatethe sharing parameters; and Download Param Flow by which participantswith permission obtain the updated sharing parameters.
 4. The method ofclaim 3, wherein the task execution flow model comprises five flow typesof external encapsulation interfaces through which sharing parametersare uploaded or downloaded by the users and are updated by the miners,and transactions generated after the flows run form a block which isencapsulated and linked into the blockchain; wherein the sharingparameters from the interfaces are trained immediately, and theinterfaces are integrated by the users to automate the entire processwithout manual confirmation.
 5. The method of claim 1, wherein, in theencryption layer, participants encrypt the training parameters using thePaillier encryption algorithm, and decrypt the updated encrypted sharingparameters into original parameter forms; meanwhile, the encryptionlayer further provides static interfaces for miners to update thesharing parameters, and employers are allowed to call the interfaces toupdate the sharing parameters and gain rewards in the blockchain.
 6. Themethod of claim 1, wherein, in blockchain layer, Corda is selected asthe blockchain platform and used to build and release a new generationof distributed applications CorDapps, and the processing flows areallowed to be written programmatically; users send requests to call eachflow in the blockchain layer through a Remote Procedure Call (RPC)interface, and a transaction is created during the execution of theflow, and an old state is consumed and a new state is constructed; afterfilling of the information, the identities of the participants areconfirmed by signatures of the participants, and then a validity of thetransaction is verified through the contract; if confirmed, theinformation is written into the blockchain layer, and the blockchainensures a consistency of the information stored by all nodes based on aconsensus mechanism provided by the Corda.
 7. The method of claim 1,wherein, the training algorithm layer acquires the updated encryptedsharing parameters in the blockchain layer, and then decrypts theupdated encrypted sharing parameters through the encryption layer, andthe new parameters obtained directly participate in the training of thetraining algorithm layer.